Search (5059 results, page 1 of 253)

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Abstract

Most Web search tools integrate sponsored results with results from their internal editorial database in providing results to users. The goal of this research is to get a better idea of how much of the screen real estate displays real editorial results as compared to sponsored results. The overall average results are that 40% of all results presented on the first screen are real results, and when the entire first Web page is considered, 67% of the results are nonsponsored results. For general search tools such as Google, 56% of the first screen and 82% of the first Web page contain nonsponsored results. Other results include that query structure makes a significant difference in the percentage of nonsponsored results returned by a search. Similarly, the topic of the query also can have a significant effect on the percentage of sponsored results displayed by most Web search tools.

Date

22. 7.2006 16:32:57

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Abstract

Describes the design and implementation of a system for computer generation of linked HTML documents to support information retrieval and hypertext applications on the WWW. The system does not require text query input, nor any client or host processing other than hypertext linkage. The goal is to construct a fully automatic system in which original text documents are read and processed by a computer program that generates HTML files, which can be used immediately by Web browsers to search and retrieve the original documents. A user with a large collection of information: for instance, newspaper articles; can feed these documents to this program and produce directly the necessary files to establish WWW home page and related pages, to support interactive retrieval and distribution of the original documents

Date

1. 8.1996 22:13:07

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Abstract

Language barrier is the major problem that people face in searching for, retrieving, and understanding multilingual collections on the Internet. This paper deals with query translation and document translation in a Chinese-English information retrieval system called MTIR. Bilingual dictionary and monolingual corpus-based approaches are adopted to select suitable tranlated query terms. A machine transliteration algorithm is introduced to resolve proper name searching. We consider several design issues for document translation, including which material is translated, what roles the HTML tags play in translation, what the tradeoff is between the speed performance and the translation performance, and what from the translated result is presented in. About 100.000 Web pages translated in the last 4 months of 1997 are used for quantitative study of online and real-time Web page translation

Date

16. 2.2000 14:22:39

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Abstract

Query translation is a viable method for cross-language information retrieval (CLIR), but it suffers from translation ambiguities caused by multiple translations of individual query terms. Previous research has employed various methods for disambiguation, including the method of selecting an individual target query term from multiple candidates by comparing their statistical associations with the candidate translations of other query terms. This paper proposes a new method where we examine all combinations of target query term translations corresponding to the source query terms, instead of looking at the candidates for each query term and selecting the best one at a time. The goodness value for a combination of target query terms is computed based on the association value between each pair of the terms in the combination. We tested our method using the NTCIR-3 English-Korean CLIR test collection. The results show some improvements regardless of the association measures we used.

Date

26.12.2007 20:22:38

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Abstract

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

Date

20. 1.2015 18:30:22

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Date

30. 3.2001 13:32:22

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Abstract

In this article, we propose new word sense disambiguation strategies for resolving the senses of polysemous query terms issued to Web search engines, and we explore the application of those strategies when used in a query expansion framework. The novelty of our approach lies in the exploitation of the Web page PageRank values as indicators of the significance the different senses of a term carry when employed in search queries. We also aim at scalable query sense resolution techniques that can be applied without loss of efficiency to large data sets such as those on the Web. Our experimental findings validate that the proposed techniques perform more accurately than do the traditional disambiguation strategies and improve the quality of the search results, when involved in query expansion.

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Abstract

Describes a novel visual user interface, WebIFQ (Web-In-Frame-Query), to assist users in specifying queries and visualising query criteria including document metadata, strucutres, and linkage information. WebIFQ automatically generates corresponding query statements for WebDB. As a result, users are not required to be aware of underlying complex schema design and language syntax. WebDB supports automated query relaxation to include additional terms related by semantic or co-occurence relationship. WebIFQ can facilitate users to reformulate queries perpetually in an interactive mode

Date

1. 8.1996 22:08:06

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Abstract

Academic search concerns the retrieval and profiling of information objects in the domain of academic research. In this paper we reveal important observations of academic search queries, and provide an algorithmic solution to address a type of failure during search sessions: null queries. We start by providing a general characterization of academic search queries, by analyzing a large-scale transaction log of a leading academic search engine. Unlike previous small-scale analyses of academic search queries, we find important differences with query characteristics known from web search. E.g., in academic search there is a substantially bigger proportion of entity queries, and a heavier tail in query length distribution. We then focus on search failures and, in particular, on null queries that lead to an empty search engine result page, on null sessions that contain such null queries, and on users who are prone to issue null queries. In academic search approximately 1 in 10 queries is a null query, and 25% of the sessions contain a null query. They appear in different types of search sessions, and prevent users from achieving their search goal. To address the high rate of null queries in academic search, we consider the task of providing query suggestions. Specifically we focus on a highly frequent query type: non-boolean informational queries. To this end we need to overcome query sparsity and make effective use of session information. We find that using entities helps to surface more relevant query suggestions in the face of query sparsity. We also find that query suggestions should be conditioned on the type of session in which they are offered to be more effective. After casting the session classification problem as a multi-label classification problem, we generate session-conditional query suggestions based on predicted session type. We find that this session-conditional method leads to significant improvements over a generic query suggestion method. Personalization yields very little further improvements over session-conditional query suggestions.

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Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

Content

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Abstract

The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

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Abstract

This study investigated a relatively unexamined query type, queries composed of URLs. The extent, variation, and user click-through behavior was examined to determine the intent behind URL queries. The study made use of a search log from which URL queries were identified and selected for both qualitative and quantitative analyses. It was found that URL queries accounted for ?17% of the sample. There were statistically significant differences between URL queries and non-URL queries in the following attributes: mean query length; mean number of tokens per query; and mean number of clicks per query. Users issuing such queries clicked on fewer result list items higher up the ranking compared to non-URL queries. Classification indicated that nearly 86% of queries were navigational in intent with informational and transactional queries representing about 7% of URL queries each. This is in contrast to past research that suggested that URL queries were 100% navigational. The conclusions of this study are that URL queries are relatively common and that simply returning the page that matches a user's URL is not an optimal strategy.

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Abstract

The aim of the work described in this paper is to evaluate the influencing effects of query-biased summaries in web searching. For this purpose, a summarisation system has been developed, and a summary tailored to the user's query is generated automatically for each document retrieved. The system aims to provide both a better means of assessing document relevance than titles or abstracts typical of many web search result lists. Through visiting each result page at retrieval-time, the system provides the user with an idea of the current page content and thus deals with the dynamic nature of the web. To examine the effectiveness of this approach, a task-oriented, comparative evaluation between four different web retrieval systems was performed; two that use query-biased summarisation, and two that use the standard ranked titles/abstracts approach. The results from the evaluation indicate that query-biased summarisation techniques appear to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach. The same methodology was used to compare the effectiveness of two of the web's major search engines; AltaVista and Google.

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Abstract

The process of term selection for query expansion by end-users is discussed within the context of a study of interactive query expansion in a relevance feedback environment. This user study focuses on how users' perceive and understand term relationships, such as hierarchical and associative relationships, in their searches

Date

30. 3.2001 13:35:22

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Abstract

User query data played an important role in the development of an automated still picture retrieval system at the National Library of Medicine. Describes backgroun information about the NLM collection and its users, describes typical user queries, and portrays representative queries. It identifies a particular picture query type, called the 'image construct query', based on an analysis of user query data. Describes difficulties in handling image construct queries by existing conventional access systems, and it proposes improves cataloging strategy combined with picture surrogates as the most effective way to generate better image retrieval

Pages

S.7-22

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Abstract

The World Wide Web has become one of our more important information sources, and commercial search engines are the major tools for locating information; however, it is not enough for a Web page to be indexed by the search engines-it also must rank high on relevant queries. One of the parameters involved in ranking is the number and quality of links pointing to the page, based on the assumption that links convey appreciation for a page. This article presents the results of a content analysis of the links to two top pages retrieved by Google for the query "jew" as of July 2004: the "jew" entry on the free online encyclopedia Wikipedia, and the home page of "Jew Watch," a highly anti-Semitic site. The top results for the query "jew" gained public attention in April 2004, when it was noticed that the "Jew Watch" homepage ranked number 1. From this point on, both sides engaged in "Googlebombing" (i.e., increasing the number of links pointing to these pages). The results of the study show that most of the links to these pages come from blogs and discussion links, and the number of links pointing to these pages in appreciation of their content is extremely small. These findings have implications for ranking algorithms based on link counts, and emphasize the huge difference between Web links and citations in the scientific community.

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Abstract

The WebQuery system offers a powerful new method for searching the Web based on connectivity and content. Examines links among the nodes returned in a keyword-based query. Rankes the nodes, giving the highest rank to the most highly connected nodes. By doing so, finds hot spots on the Web that contain information germane to a user's query. WebQuery not only ranks and filters the results of a Web query; it also extends the result set beyond what the search engine retrieves, by finding interesting sites that are highly connected to those sites returned by the original query. Even with WebQuery filering and ranking query results, the result set can be enormous. Explores techniques for visualizing the returned information and discusses the criteria for using each of the technique

Date

1. 8.1996 22:08:06

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Abstract

Caching of query results is an important mechanism for efficiency and scalability of web search engines. Query results are cached and presented in terms of pages, which typically include 10 results each. In navigational queries, users seek a particular website, which would be typically listed at the top ranks (maybe, first or second) by the search engine, if found. For this type of query, caching and presenting results in the 10-per-page manner may waste cache space and network bandwidth. In this article, we propose nonuniform result page models with varying numbers of results for navigational queries. The experimental results show that our approach reduces the cache miss count by up to 9.17% (because of better utilization of cache space). Furthermore, bandwidth usage, which is measured in terms of number of snippets sent, is also reduced by 71% for navigational queries. This means a considerable reduction in the number of transmitted network packets, i.e., a crucial gain especially for mobile-search scenarios. A user study reveals that users easily adapt to the proposed result page model and that the efficiency gains observed in the experiments can be carried over to real-life situations.

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Abstract

The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of 2 similarly performing systems is superior. For both a single query terms and for a multiple query term retrieval model, a model for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Describes predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering. Simulation illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems

Date

22. 2.1996 13:14:10

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Abstract

Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web

Date

1. 8.1996 22:08:06